Pythium oligandrum is a potential biocontrol agent to control a wide range of fungal and oomycete-caused diseases, such as Pythium myriotylum-caused rhizome rot in ginger, leading to reduced yields and compromised quality. Previously, P. oligandrum has been studied for its plant growth-promoting potential by auxin production and induction of disease resistance by elicitors such as oligandrin. Volatile organic compounds (VOCs) play beneficial roles in sustainable agriculture by enhancing plant growth and resistance. We investigated the contribution of P. oligandrum-produced VOCs on plant growth and disease suppression by initially using Nicotiana benthamiana plants for screening. P. oligandrum VOCs significantly enhanced tobacco seedling and plant biomass contents. Screening of the individual VOCs showed that 3-octanone and hexadecane promoted the growth of tobacco seedlings. The total VOCs from P. oligandrum also enhanced the shoot and root growth of ginger plants. Transcriptomic analysis showed a higher expression of genes related to plant growth hormones and stress responses in the leaves of ginger plants exposed to P. oligandrum VOCs. The concentrations of plant growth hormones such as auxin, zeatin, and gibberellic acid were higher in the leaves of ginger plants exposed to P. oligandrum VOCs. In a ginger disease biocontrol assay, the VOC-exposed ginger plants infected with P. myriotylum had lower levels of disease severity. We conclude that this study contributes to understanding the growth-promoting mechanisms of P. oligandrum on ginger and tobacco, priming of ginger plants against various stresses, and the mechanisms of action of P. oligandrum as a biocontrol agent.The oomycete IMPORTANCE Plant growth promotion plays a vital role in enhancing production of agricultural crops, and Pythium oligandrum is known for its plant growth-promoting potential through production of auxins and induction of resistance by elicitors. This study highlights the significance of P. oligandrum-produced VOCs in plant growth promotion and disease resistance. Transcriptomic analyses of leaves of ginger plants exposed to P. oligandrum VOCs revealed the upregulation of genes involved in plant growth hormone signaling and stress responses. Moreover, the concentration of growth hormones significantly increased in P. oligandrum VOC-exposed ginger plants. Additionally, the disease severity was reduced in P. myriotylum-infected ginger plants exposed to P. oligandrum VOCs. In ginger, P. myriotylum-caused rhizome rot disease results in severe losses, and biocontrol has a role as part of an integrated pest management strategy for rhizome rot disease. Overall, growth enhancement and disease reduction in plants exposed to P. oligandrum-produced VOCs contribute to its role as a biocontrol agent. Pythium oligandrum, a non-plant-pathogenic oomycete, has been studied for its antagonistic effects against plant pathogens  and phytohormones –4. Pythi–athogens , 6 as weathogens  or by inathogens . Previou pinodes , Pythiumxysporum . Moreoveriotylum . P. myri disease –14, dampdisease –, and crodisease –.Bacillus subtilis promoted Arabidopsis thaliana growth via the cytokinin signaling pathway  signaling pathway-dependent mechanism by regulating stomatal opening and closing  on N. benthamiana seedlings, a bipartite petri plate was used to prevent diffusion of secreted growth-promoting compounds through the agar. After 10 days, there were significant increases in shoot length (2.25-fold) and seedling dry weight (5.5-fold) in the seedlings exposed to Po-VOCs compared to the control , number of root tips (2.2-fold), total root length (2.2-fold), and average root diameter (3.5-fold) compared to the control plants  and gibberellin-regulated protein (NbGA14 [5.4-fold]) in N. benthamiana plants exposed to Po-VOC compared to the control  and plant dry weight (3.3-fold) of Po-VOC-exposed ginger plants compared to the control  and volume (2.5-fold) were larger than those for root length (2.2-fold) and number of root tips (2.2-fold)  increase differed between .2-fold) . In ging.2-fold) .P. oligandrum VOCs lead to the enhancement of plant growth in ginger, transcriptome sequencing (RNA-seq) analysis was performed. Ginger leaves were sampled after 21 days of Po-VOC exposure and compared to those of the ginger plants grown in the absence of Po-VOCs. Principal-component analysis (PCA) showed a clear separation between the three ginger plants that were exposed to Po-VOCs and the three control ginger plants grown without exposure to Po-VOCs , there were 1,661 genes upregulated and 1,404 genes were downregulated  based on Pfam domains that were used for functional annotation of ginger genes . The ginger genome contains at least 141 genes annotated with the Pfam domain for the auxin/indole-3-acetic acid (AUX/IAA) family (PF02309). The transcription of genes belonging to the AUX/IAA family can be upregulated by the plant hormone auxin . A substMyb and WRKY genes can be involved in the regulation of defense and stress responses . The esponses , 43. In esponses . The genGrowth hormones were selected for quantification in ginger leaf blades based on the upregulation of genes related to these hormones. Overall, our results showed that exposure to Po-VOCs increased the concentration of plant growth-related hormones in ginger. There was a significant increase in hormone concentration in leaf blades after 7, 14, and 21 days of Po-VOC-exposed ginger plants compared to the control. The concentration of indole-3-acetic acid (IAA) was increased significantly by 2.5-fold after 7 days and 3-fold after 14 and 21 days in ginger plants exposed to Po-VOCs compared to their controls ; Fig. S5P. oligandrum GAQ1-produced VOCs (which had a relative percentage of area of >1% in the gas chromatagraphy-mass spectrometry [GC-MS] chromatogram) identified from our previous study . Although there was no increase in growth from the other 10 VOCs, they at least did not significantly reduce the seedling dry weight . Both hexadecane and 3-octanone increased N. benthamiana seedling growth more at a concentration of 50 μM than at 10 μM, with maximum increases in dry weight of 2.7-fold for hexadecane and 3-fold for 3-octanone . Moreover, the GC-MS chromatograph of P. myriotylum SWQ7 showed that both hexadecane and 3-octanone were not detected and standards were also used to confirm the identity of both VOCs .GC-MS analysis was conducted to identify whether the two growth-promoting VOCs hexadecane and 3-octanone produced by P. oligandrum-produced VOCs on the disease severity in ginger, the plants infected with the soft rot pathogen P. myriotylum were exposed to P. oligandrum VOCs. After 21 days of exposure to Po-VOCs, the plants were photographed, and the disease severity index was calculated. Our results showed that 75% disease severity was observed in the P. myriotylum-infected ginger plants without VOC exposure, while those exposed to P. oligandrum VOCs had a much lower disease severity index of 15% , an auxin-like compound  were upregulated in Po-VOC-exposed ginger plants. The plant hormone auxin can trigger the transcription of genes belonging to the AUX/IAA family  in tobacco was shown to enhance resistance against Pythium spp.  was upregulated in ginger plants infected with Fusarium oxysporum , Myb-like DNA-binding domain (PF00249) and MYB-CC type transfactor LHEQLE motif (PF14379), and AP2 domain (PF00847) were upregulated. WRKY transcription factors can be important for the regulation of genes associated with plant defense responses. Our results showed that genes annotated with the Pfam domain for the WRKY DNA-binding domain were upregulated in Po-VOC-exposed ginger plants. Previously, the overexpression of ium spp. . It has xysporum . In gingd ginger . In Po-Vd ginger .P. oligandrum, reported in our previous study  was described previously for its biocontrol activity  and grown under the growth conditions described above.The activity , 76. Theeviously  as a pateviously . All of P. oligandrum on seedling growth, five N. benthamiana seedlings of equal size were placed on one side of a bipartite petri plate containing Murashige and Skoog (MS) medium , a 5-mm mycelial plug of actively growing P. oligandrum was placed on the other side of the petri plate containing V8 agar medium, and the plates without P. oligandrum were used as a control. The petri plates were sealed by wrapping them at least nine times with Parafilm  to avoid the escape of VOCs and placed in an environmentally controlled chamber with 16-h-light and 8-h-dark cycles at 25°C. The seedling shoot length and dry weight were measured after 10 days. To measure the dry weight, plant material was dried in an oven until it reached a constant weight, which indicated that all moisture was removed. The experiment was repeated three times, each time with three replicates, and each replicate included five seedlings. Root morphological parameters were measured using a RhizoScan scanner  and analyzed by WinRHIZO software  (To investigate the effect of VOCs produced by  Canada) .P. oligandrum GAQ1 VOCs (Po-VOCs) on N. benthamiana and ginger in planta was evaluated by the method described previously  to allow the roots to be exposed to the VOCs produced by P. oligandrum. Two sterilized filter paper pieces  were placed inside the bottom of the plastic pot to avoid the leakage of contaminating liquid through the holes into the glass jar. Equal-sized N. benthamiana or ginger seedlings (one in each pot) were transferred to the plastic pots containing sterilized vermiculite and organic matter (3:1) and placed on the glass jars containing either P. oligandrum or only V8 agar medium as control. The pot-jar assembly was then firmly sealed by wrapping it at least nine times with parafilm  to avoid the escape of VOCs produced by P. oligandrum. The pot-jar assembly was then subjected to 16-h-light and 8-h-dark cycles at 25°C and watered with sprayer. Plants were watered by wetting the surface of the soil to avoid contamination by leakage of water through holes into the glass jar. Plants were grown for 21 days, and growth was evaluated by the difference in shoot lengths (from the base of the stem to the top of the straightened leaves), dry weights of the whole plant, and root morphological parameters. Leaf material was collected and frozen at −80°C for subsequent analysis of gene expression and hormone levels. For the gene expression and hormone analyses, separate experiments were performed. Three biological replicate leaf samples were collected for each treatment: i.e., three biological replicate samples for VOC-exposed plants and three biological replicate samples for nonexposed (control) plants.The effect of eviously , and a dA260/280 and A260/230, whereby for all the samples, A260/280 was greater than 2.0 and A260/230 was greater than 1.8, and RNA integrity was evaluated using the Agilent 2100 bioanalyzer . The libraries were constructed using the TruSeq stranded mRNA LT sample prep kit  according to the manufacturer’s instructions.For transcriptome analysis, the ginger plants were grown as described in the previous section. The leaf blades from the two uppermost leaves of the plants grown for 21 days with and without exposure to Po-VOCs were collected from a separate experiment from that used for hormone analysis, flash-frozen in liquid nitrogen, and stored at −80°C. For the RNA sequencing analysis, three biological replicate samples were collected for VOC-exposed and nonexposed (control) plants. Total RNA was extracted using the TRIzol reagent according to the manufacturer’s protocol. RNA purity and quantity were evaluated using the NanoDrop 2000 spectrophotometer , and the parameter to check the quality of RNA samples included measuring the absorbances of Zingiber officinale reference genome (GCF_018446385.1_Zo_v1.1_genomic.fna)  of <0.05, fold change (FC) of >2 or <0.5, and an FPKM of >1 in the up- or downregulated genes were set as the threshold for significantly differential expression. The PCA was performed using the R statistical environment. The ginger proteins were annotated with Pfam domains using the InterProScan functional annotation tool  using the default settings at https://usegalaxy.eu  of fastq format were first processed using Trimmomatic , and themic.fna)  using HImic.fna) , and >90mic.fna)  of each mic.fna) , and thealaxy.eu –88. The N. benthamiana leaves of genes annotated as encoding expansins (NbEXPA1 and NbEXPA2) and gibberellin-regulated protein (NbGA14) was measured. Expansin genes NbEXPA1 and NbEXPA2 were amplified using primers from references NbGA14 gene was amplified using primers from reference N. benthamiana leaves (excluding the petiole) were harvested from plants after 21 days of exposure to Po-VOCs, and the plants without VOC exposure served as the control. The qPCR validation for the ginger RNA-seq was performed using the same RNA samples used for RNA-seq. cDNA was synthesized using the EasyScript one-step genomic DNA (gDNA) removal and cDNA synthesis kit (TransGen Biotech), using random primers according to the manufacturer’s instructions. The qPCR was performed with a LightCycler 96 instrument  using SYBR green Pro Taq HS premix  with NbEF-1α (ZoEF-1α (−△△CT) method (The expression in  NbEF-1α  and ZoEF(ZoEF-1α  as an in) method . For gin) method  at NCBI Ginger plants exposed to Po-VOCs were analyzed for the concentrations of the plant growth-regulating hormones gibberellic acid (GA3), indole 3-acetic acid (IAA), zeatin (ZEA), and abscisic acid (ABA). Leaf blades were collected at three time points of 7, 14, and 21 days from both Po-VOC-exposed and nonexposed ginger plants (grown as described in the previous section). For the extraction of the hormones, 0.1 g of fresh (not dried) leaf blades from each treatment was ground to a fine powder using a TissueLyser (60 s at 50 Hz). The fine leaf powder was then used to prepare the samples for hormone quantification based on a method described previously . BrieflyP. oligandrum VOCs identified through previous GC-MS analysis based on their relative percentage of area of >1%  was used as a solvent to obtain final concentrations of 10, 50, and 100 μM. N. benthamiana seedlings were grown on MS agar medium on one side of the bipartite plate as described in the previous section, and on the other side of the plate, 50 μL of each of the VOCs was applied to 1-cm-diameter filter papers. Control plates were prepared in the same manner, except the filter papers were wetted with only DMSO. After 10 days of VOC exposure, the dry weight of the seedlings was measured.The a of >1%  were evaP. oligandrum strains GAQ1 and CBS 530.74 and P. myriotylum strain SWQ7. Five-millimeter-diameter mycelial plugs from an actively growing mycelial mat of either P. oligandrum  or P. myriotylum SWQ7 was inoculated on V8 agar medium in 100-mL vials. Noninoculated V8 agar medium was used as a control. The absence of a VOC from the noninoculated V8 agar medium control was critical to assigning a VOC as actually produced by P. oligandrum or P. myriotylum. The vials were closed with rubber lids and were firmly sealed by wrapping them at least nine times with parafilm , and then the vials were incubated at 25°C for 3 days before VOC collection. The SPME fiber was injected into the headspace of the vial containing either P. oligandrum GAQ1 or CBS 530.74 or P. myriotylum SWQ7 and incubated at 30°C for 30 min. GC-MS analysis was carried out using a TSQ 8000 EVO gas chromatograph , and VOCs were identified as described in reference Solid-phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) analysis was conducted to identify VOCs produced by P. oligandrum GAQ1 was inoculated in the glass jars containing V8 agar medium, while the glass jars containing only V8 agar medium were used as the control. The plastic pots were prepared as described in the previous section. The roots were inoculated with autoclaved wheat seeds, which were used to culture P. myriotylum SWQ7 isolate or controls at the same time as transplantation of equal-sized seedlings of either N. benthamiana or ginger. The wheat seed inoculum was prepared by the method described previously  = [Σ (ni × vi)/(V × N)] × 100, where ni indicates the number of plants with the respective disease rating, vi is the disease rating, V is the highest disease rating  using SPSS software version 25, and the significant difference between the means was determined using Tukey’s honestly significant difference (HSD) test after one-way ANOVA.Generally, the data were statistically analyzed using either Student’s GSE235182.The RNA-seq reads from this project were submitted to the GEO database under accession no.


There are multiple routes to prepare semi-solid slurries with a globular microstructure for semi-solid forming. The variations in the microstructure of semi-solid slurries prepared using different routes may lead to significant differences in the flow behavior and mechanical properties of rheo-diecasting parts. Therefore, it is crucial to have a comprehensive understanding of the microstructure evolution associated with different slurry preparation routes and their resulting effects. In this study, the gas-induced semi-solid process (GISS) and the swirl enthalpy equilibrium device (SEED) routes were employed to prepare semi-solid Al-Si-Mg slurries for their simplicity and productivity in potential industrial applications. The prepared slurries were then injected into the shoot sleeves of a high-pressure die casting (HPDC) machine to produce tensile test bars. Subsequently, the bars underwent T6 treatment to enhance their mechanical properties. The microstructure, segregation, and mechanical properties of the samples were investigated and compared with those of conventional HPDC. The results indicated that the GISS and SEED can produce semi-solid slurries containing a spherical α-Al primary phase, as opposed to the dendritic structure commonly found in conventional castings. The liquid fraction had a significant effect on the flow behavior, resulting in variations in liquid segregation and mechanical properties. It was observed that a higher solid fraction (>75%) had a suppressing effect on surface liquid segregation. In addition, the tendency for liquid segregation gradually increased along the filling direction due to the special flow behavior of the semi-solid slurry with a low solid fraction. Furthermore, under the same die-casting process parameters, the conventional HPDC samples exhibit higher yield stress (139 ± 3 MPa) compared to SEED-HPDC and GISS-HPDC samples, which may be attributed to the small grain size and the distribution of eutectic phases. After undergoing the T6 treatment, both SEED-HPDC and GISS-HPDC samples showed a significant improvement in yield and tensile strength. These improvements are a result of solution and precipitation strengthening effects as well as the spheroidization of the eutectic Si phase. Moreover, the heat-treated SEED-HPDC samples demonstrate higher ultimate strength (336 ± 5 MPa) and elongation (13.7 ± 0.3%) in comparison to the GISS-HPDC samples  after heat treatment, mainly due to their low porosity density. These findings suggest that both GISS-HPDC and SEED-HPDC processes can be utilized to produce parts with favorable mechanical properties by implementing appropriate heat treatments. However, further investigation is required to control the porosities of GISS-HPDC samples during heat treatment.  High-pressure die-casting (HPDC) technology is a highly productive near-net-shape forming process used for aluminum and magnesium alloys. It has found applications in various industries, such as automotive, aerospace, and 5G communications ,2. BlistIn the rheo-HPDC process, the solid phase and liquid phase exhibit different flow behavior, which leads to segregation phenomena. Feng  utilizedOne of the key factors affecting rheo-HPDC is the preparation of semi-solid slurries with a uniform and spherical microstructure. Since Flemings obtained a semi-solid slurry with primary spherical grains by stirring Sn-15%Pb alloy in the 1970s, researchers have invented some new methods to prepare semi-solid slurries . Alcan  patentedAlthough many researchers have studied the application of GISS and SEED in different alloys and demonstrated their potential for industrialization, there are fewer studies on the two processes in relation to the forming and mechanical properties of the same alloy. Therefore, the aim of this study is to comparatively investigate the microstructure, flow behavior, and mechanical properties of the alloy produced by different rheo-HPDC processes. In this study, the semi-solid Al-Si-Mg alloy slurries were prepared by the GISS and SEED routes. The prepared slurries were then injected into the shoot sleeves of a high-pressure die casting (HPDC) machine to produce tensile test bars. Subsequently, the bars underwent T6 treatment to enhance their mechanical properties. The differences in the slurry microstructure of the two were examined. The segregation and mechanical properties of the samples produced by GISS-HPDC and SEED-HPDC were compared and analyzed. 2Cl6 and held for 15 min. Al-5Ti-B master alloy (1 wt.%) was added to refine the grains, and the melt was held for 15 min before starting the semi-solid slurry preparation. The chemical compositions of the GISS and SEED slurries were determined separately, as presented in The Al-Si-Mg aluminum alloy used in this study was prepared using purity aluminum (4 N), Si (7 N), and Al-Mg master alloy (Al-20 wt.% Mg). The alloy was processed according to the procedure depicted in 3/h through a ceramic rod. The ceramic rod was immersed in the melt at a temperature of 625 °C for 4 min, with a revolution speed of 90 r/min and a rotation speed of 120 r/min. After the stirring, the slurry was held for 85 s. After completing the slurry preparation, it was transferred to a shoot sleeve of the HPDC machine for the production of parts with varying injection speeds, as depicted in As illustrated in During the SEED-HPDC process, the melt was poured into a steel crucible at a temperature of 625 °C. It was then rotated eccentrically at 150 r/min for 100 s. The resulting semi-solid slurry was transferred to the shot sleeve to obtain castings using the same die-casting process parameters as the GISS-HPDC process. The details of the T6 heat treatment were as follows: the test bars were subjected to a solution treatment at a temperature of 540 °C for 0.5 h in a furnace (CGF1200-25) with a temperature fluctuation of ±1 °C. After the solution treatment, they were quenched in 60 °C water. Subsequently, the quenched bars were transferred to a furnace (DHG-9076A) with a temperature fluctuation of ±1 °C for aging at 170 °C for 6 h.α radiation at a speed of 10° per minute between 10° and 90°. The solid fraction, average grain size (https://imagej.nih.gov/ij/index.html). The solid fraction (Fs) was estimated by measuring the volume fraction of the large light gray α-Al grains, which were preserved during quenching from a semi-solid state. The detailed procedure for the measurement is given in [Metallographic samples of slurry and parts were ground, polished, and then etched using a 0.5% HF solution. The microstructure was observed using a Nikon metallographic microscope . Phase identification and fracture morphology were analyzed using a JEOL-7610F scanning electron microscope (SEM). The phase analysis of the samples was conducted by X-ray diffraction  with Cu-Kgiven in . During 1-Al grains. The results indicate that the semi-solid slurry prepared by GISS exhibits characteristics of a 55% solid fraction, an average grain size of 105 μm, and an average shape factor of 0.85. On the other hand, the SEED method results in a slurry with a 75% solid fraction, an average grain size of 95 μm, and an average shape factor of 0.60. Additionally, the α2-Al grains and eutectic Si phase in the slurries prepared by these two methods are also distributed around α1-Al. The α2-Al grains are formed by the solidification of the residual melt during the die-casting process. The grain size of α2-Al grains is much smaller compared to α1-Al grains. The number of α2-Al grains in GISS slurry is much higher than that in SEED slurry, as shown in 1-Al grains prepared by the GISS method is predominantly spherical, while in SEED, α1-Al grains exhibit a more complex and irregular morphology, appearing near spherical or rosette-shaped. Although both semi-solid slurry preparation methods operate under a nucleation-controlled mechanism, there are certain differences in the slurry produced using the two preparation methods. The use of a graphite crucible in GISS results in a lower cooling rate, more uniform temperature, and solute distribution in the melt, leading to the formation of more spherical α1-Al grains. Additionally, GISS involves a longer holding time, which contributes to larger α1-Al grains. Tang [The morphology of αns. Tang  observedns. Tang  discover1-Al grains in SEED is influenced by various parameters such as pouring temperature and eccentric rotation speed and time. The combined effects of high grain density, slow cooling, and rotational convection reduce temperature and solute gradients at the solidification interface, allowing grains to grow in a spherical morphology. Qu [The spheroidization mechanism of the GISS process involves the combined stirring effect of a stirring rod and argon. This results in a more uniform temperature and solute distribution in the melt, leading to nearly equal growth rates in all directions. This uniform growth is beneficial for the spherical growth of grains . The forlogy. Qu  develope1-Al grains gradually decreases along the filling direction of the slurry, and a large number of small α2-Al grains surround the spherical α1-Al grains. On the other hand, 1–e1) illustrates the SEED-HPDC microstructures at corresponding positions. It can be seen that the microstructure is evenly distributed in all positions, with no significant difference in the proportion of α1-Al grains. The microstructure of the part is similar to that of the slurry. In order to investigate the flow behavior and segregation during the rheo-HPDC process, the microstructure changes in parts of GISS-HPDC and SEED-HPDC were analyzed. 1-Al grains in different positions , which have a detrimental effect on mechanical properties [The transformation of fiber-shaped eutectic Si into near-globular eutectic Si can reduce the lacerating effect of eutectic Si on the matrix and improve the mechanical properties. Chen  investigoperties . The presence of Fe-intermetallic compounds in aluminum alloys can be detrimental as they can cause damage to the aluminum matrix, create stress concentration, and reduce the mechanical properties of the aluminum alloy . FurtherThe dendritic microstructure in conventional HPDC samples can be effectively replaced by spherical primary α-Al grains through GISS and SEED methods combined with rheo-HPDC. The slurry prepared by SEED, which has a lower liquid fraction (~25 ± 4%), exhibits a smaller grain size (95 ± 4.2 μm) and denser eutectic Si phases compared to the slurry prepared by GISS. However, the shape factor of the α-Al grains prepared by SEED (~0.60) is lower as compared to that of GISS (~0.8).The phenomenon of surface liquid segregation is observed in both SEED-HPDC and GISS-HPDC samples. Furthermore, in the GISS-HPDC samples, there is a gradual increase in the liquid fraction along the filling direction. However, the SEED-HPDC samples with a low liquid fraction (<25%) in the rheo-HPDC process with proper process parameters help to minimize the occurrence of segregation. As a result, these slurries with low liquid fractions enable the production of parts with a more uniform microstructure.Both heat-treated GISS-HPDC and SEED-HPDC samples exhibit noticeably higher strength and elongation when compared to conventional HPDC samples. Moreover, the SEED-HT samples demonstrate superior mechanical properties in comparison to the GISS-HT samples, owing to their denser structure and reduced porosity. The yield strength, tensile strength, and elongation of the SEED-HT samples are measured at 249 MPa, 336 Mpa, and 13.7%, respectively.Although good mechanical properties were obtained for the GISS-HPDC samples after heat treatment, further investigation is needed to minimize porosities during this process. Additionally, it is important to study the improvement of the shape factor for primary α-Al grains in SEED slurries and its impact on flow behavior.The tensile test bars of the Al-Si-Mg aluminum alloy were successfully manufactured by GISS-HPDC and SEED-HPDC. The GISS and SEED methods were employed to prepare semi-solid slurries. After investigating and analyzing the microstructure, segregation, and mechanical properties of the samples, the following conclusions were drawn:


We report the opticalproperties of GaSbBi layers grown on GaSb(100) substrates with different bismuth contents of 5.8 and 8.0% Bi.Fourier-transform photoluminescence spectra were determined to identifythe band gaps of the studied materials. Further temperature- and power-dependentphotoluminescence measurements indicated the presence of localizedstates connected to bismuth clustering. Finally, time-resolved photoluminescencemeasurements based on single-photon counting allowed the determinationof characteristic photoluminescence decay time constants. Becauseof the increasing bismuth content and clustering effects, an increasein the time constant was observed. Anotherapproach for detecting multiple gases simultaneously uses dual-combspectroscopy, as it was conducted in laboratory conditions in ref.4 The generation of optical frequency combs canbe achieved through the mode-locking technique.5 The fabrication of mid-infrared mode-locked lasers wasonly recently proven possible,6 and extensivework is still required for the development and engineering of newmaterial systems.Optical sensing systemsfor applications in the infrared spectrumtypically consist of a laser source, operating preferably in singlemode, such as diode lasers (DLs),8 Furthermore, in comparison to the dilute III–V nitrides,they offer interesting benefits such as preserved electron mobility.9 Such properties make III–V Bi semiconductorsattractive for optoelectronic devices operating in the near- and mid-infraredspectral ranges. However, the large size of the Bi atom and its natureto bond weakly to group III atoms makes it difficult to incorporateBi into the III–V lattice, and thus Bi tends to segregate10 to the crystal growth surface and/or evaporate.11 A yet emerging alloy in the III–V Bifamily is GaSbBi, which has attracted attention due to its potentialapplications in the mid-IR region. However, so far, most studies havefocused on the growth and structural properties of the novel alloy,with only a few groups demonstrating in detail their optical properties.Recently, some attention has also been paid to the investigation ofthe carrier dynamics in GaInSbBi/GaSb quantum wells.12 In this study, we focus on the investigation of the photoluminescence(PL) lifetime in bulk crystal GaSbBi layers. The results of this workmay prove valuable for further development of mid-infrared mode-locklasers, where the cavity round-trip parameter is crucial for its operation.As it was shown in ref . Two samples with differentbismuth atom concentrations, sample A—5.8% and sample B—8.0%,were investigated. Both samples were grown on 500 μm-thick (100)n-GaSb substrates using the same growth procedure as in our earlierwork.316 The signal was gatheredby a liquid-nitrogen-cooled InSb photodiode detector, while phase-sensitivedetection of the optical response was performed using a lock-in amplifier.The excitation beam was provided by a 660 nm line of a laser diodemodulated at a frequency of 800 Hz with a chopper.To measurethe Fourier-transform photoluminescence (FTPL) spectra,a vacuum-sealed Bruker Vertex 80v spectrometer was used. It operatedin step-scan mode, with an external measurement chamber and an additionalmodulated pumping laser beam.17Time-resolvedphotoluminescence measurements were recorded by asuperconducting single photon detector (SSPD) with an NbN nanowirearray (cutoff wavelength ∼2.3 μm) and TRIAX grating monochromator.The excitation beam (830 nm) was provided by a mode-locked Ti-Sapphirelaser. The laser system generates trains of ∼140 fs-long pulsesat a repetition frequency of 76 MHz. The time correlation is providedby a PicoHarp 300 correlator (PicoQuant) triggered by a laser pulse.To avoid the pile-up effect, only 5% of the detected photon countwas used to build the histograms.419 Temperature-resolved PL  is 34 K, which can be clearly observedin the temperature evolution of spectra. At 30 K, the emission fromthe state connected to the structural defects is much weaker. 2a,b For 2 clustering in thelattice structure.10 This phenomenon couldbe significant for the optical properties of the samples presentedin this article, causing the carrier trapping mechanism. We note thatthe investigated structures in this paper consist of thick layersof GaSbBi, with the PL being probed much deeper than the Raman signal.In fact, this effect could be diminished by the application of thinlayers in low-dimensional systems such as quantum wells as the numberof clusters probed by the PL would reduce.The analysis here isconsistent with Raman spectroscopy measurementspresented in a previous study on GaSbBi, where a vibrational modewas observed to be associated with BiIPL(P) = βPexcα, where IPL is the integrated PL intensity, P is the excitationpower, and β and α are fitting parameters. β isthe emission efficiency. The exponent α has the following values:α = 1 for excitonic recombination; α = 2 for free carrierrecombination. The relation of 1 < α < 2 is true for theintermediate case where both free exciton and free carrier recombinationtake place,22 while α < 1 whenemission is connected to impurity or defect-related emission.24 As can be seen, at 10 K, the α parameter is significantlylower than 1, which means that the emission at this temperature isdominated by the Bi clustering effect. At 30 K, the α parameterremains the same as that at 10 K for sample B, whereas for sampleA, it increases to almost 1. The obtained results are consistent withconclusions obtained through the analysis of the temperature-dependentPL spectra, where we suspected that the bismuth clustering effectis stronger for sample B than for sample A. In addition, a blue-shiftin the emission energy was observed, as seen in the inset of For further verificationof the previous analysis, an excitation-laser-power-dependentPL measurement was performed. The analysis of the spectra is shownin 25 or pump-probe measurements26). However, in our case, employing the SSPD detectormade TRPL experiments achievable. The utilized setup provides informationabout the time evolution of the whole PL spectrum, which can be veryconvenient in analyzing the PL lifetime of different emission states. y-axis represents the time scale for the decay process andthe x-axis the energy scale of the PL signal. Inthe insets, the normalized PL signal decays are shown for four exemplaryenergies and indicated by vertical arrows on the main graphs. Thedecay time acquired from such spectra could be presented as PL.The main objective of this researchwas to determine the radiativePL lifetimes in this novel material system by utilizing time-resolvedphotoluminescence (TRPL). The mid-infrared regime made the measurementsparticularly problematic .The determined PL lifetime in 5FTPL measurements wereperformed, confirmingthe energy gap ofboth samples, with 0.63 eV for the sample with 5.8% Bi and 0.58 eVfor 8.0% Bi. Temperature-dependent photoluminescence allowed the determinationof two activation energies for the emission peak, suggesting two recombinationprocesses occurring in close proximity in terms of energy. These valueswere assigned to bismuth cluster trapping (2.92 and 6.04 meV) andto the recombination from the GaSbBi band edge (40.10 and 43.20 meV).Excitation-laser-power-dependent photoluminescence analysis showedthat the power coefficient values at low temperatures were characteristicof defect occurrence, in contrast to spectra at temperatures abovethe activation energy of bismuth clustering. In addition, time-resolvedphotoluminescence measurements were performed as a function of energydispersion, allowing the determination of the characteristic PL lifetime.Finally, the photoluminescence decay constant changed from 50 to 100ps to 80–180 ps as the amount of incorporated bismuth atomswas increased.


Introduction: Psychiatric symptoms and fatigue are common after the coronavirus disease 2019 (COVID-19) illness. The cause of these symptoms is direct neuronal injury and indirect injury with immune-mediated inflammation. In addition, social factors also affect mental health.Objective: We aim to compare psychiatric symptoms and fatigue between COVID-19 survivors and healthy controls.Material and methods: We prospectively evaluated 100 COVID-19 survivors for anxiety, depression, positive affect, and behavior control using the Mental Health Inventory (MHI). Fatigue is assessed using the Modified Fatigue Impact Scale (MFIS) score. We compared them with 100 healthy controls.Results: There was a significant statistical difference between the MHI score and individual components of MHI. Overall, MHI scores in cases and controls were 79.41 and 93.31, respectively, with a P value of less than 0.0001. Computed scores for anxiety, depression, behavior control, and positive affect of COVID-19 survivors showed statistically significant differences as compared to healthy controls. There was a weak association between hospital stay duration and poor MHI scores. Fatigue was significantly worse in COVID-19 survivors, with a mean score of 6.93 in cases and 5.35 in controls, with a P value of 0.0001. This was a cross-sectional study evaluating psychiatric symptom scores, but not establishing the diagnosis. It is suggested that appropriate treatment and counseling for these symptoms should be done.Conclusions: Psychiatric symptoms and fatigue were significantly more common in COVID-19 patients after recovery from acute illness. It is a major contributing cause of morbidity other than organic complications of COVID-19 and requires attention in management. Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus, a positive-sense, single-stranded RNA virus. It can cause various symptoms ranging from common colds to severe acute respiratory syndrome (SARS) . In addiParticipantsAssuming a moderate effect size of 0.3 of the difference in the Mental Health Inventory (MHI) score between cases and controls at a significance level of 5% and power of 80%, the sample size was worked out to 82, which was rounded to 100. These 100 patients who survived COVID-19 (had positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19) in the last six months visited or were referred by other departments to the post-COVID-19 outpatient department (OPD). We also screened 100 healthy controls without a history of COVID-19. Controls were assumed to be free of COVID-19 infection. We excluded those with a history of fever, cough, breathlessness, or hospital admission in the past six months for similar complaints. RT-PCR test for COVID-19 was not done in controls. Both cases and controls were screened at post-COVID-19 OPD. Written consent was obtained. The study was approved by the Ethics Committee of Dr. D. Y. Patil Vidyapeeth, Pune .We evaluated the patients for psychiatric symptoms and the duration of hospital stay. For psychiatric symptoms, we used four components of the Mental Health Inventory (MHI): anxiety, depression, behavior control, and positive affect. The Mental Health Inventory developed by Veit and Ware  has 18 iWe also evaluated fatigue using the Modified Fatigue Impact Scale (MFIS). It is validated in many diseases ,11. PatiStatistical analysisStatistical analysis was done to compare depression, anxiety, behavior control, and positive affect between cases and control, as well as to analyze fatigue severity. We tried to find out the correlation between the duration of hospital stay and psychiatric symptoms. Data were entered in Microsoft Excel 2019  and analyzed using MedCalc version 18.2.1 . Nominal variables were summarized by means of proportions and interval scale variables by mean (standard deviation (SD)) if normally distributed and median (interquartile range (IQR)) if not normally distributed. Normality was assessed using the Shapiro-Wilk test. In the case of interval scale data, if it was normally distributed, then a t-test was used  and the Mann-Whitney U test if it was not normally distributed.The study results are presented in Table It demonstrates a significant difference in psychiatric symptom score and Modified Fatigue Impact Scale between cases and controls. In terms of demographic characteristics, as seen in Table As seen in Figure Figure Figure Also, as seen in Figure The Modified Fatigue Impact Scale score in cases was 6.93, and in controls, it was 5.35; it shows a significant difference.The median duration of hospital stay among cases was 14 days, with an IQR of 11.5-14 days. The association of hospital stays with mean computed MHI is shown in Figure During the COVID-19 outbreak, psychiatric symptoms were commonly reported, and even in the post-illness period, these symptoms remained prevalent.In our study, which is only a case-control study, we compared the symptoms in COVID-19 survivors on follow-up up to six months and healthy people who experienced the same time period of the COVID-19 pandemic. We found that there were significantly worse psychiatric symptoms in COVID-19 survivors on the overall MHI score  and also on an individual component of MHI compared to healthy controls. Computed anxiety score , computed depression score , computed behavior control score , and computed positive affect score  had statistically significant difference. Previous studies have shown a significant prevalence of psychiatric manifestations in post-COVID-19 patients. One study found psychiatric morbidities ranging from 10% to 35% in the post-illness stage . This waPsychiatric symptoms can be caused by two mechanisms: immune-mediated and psychological stressors. The immune response to coronaviruses induces local and systemic production of cytokines, chemokines, and other inflammatory mediators . COVID-1Cytokine dysregulation has been studied in psychiatric disorders. Elevated levels of peripheral pro-inflammatory mediators such as interleukins and C-reactive proteins (CRPs) have been reported in bipolar disorder, as well as in other mood disorders . The relPsychological stressors such as social isolation, fear of illness, stigma, and uncertainty can also cause psychiatric symptoms . A studyFatigue is also a common symptom during the post-COVID-19 period. In this study, we found significantly worse fatigue scores in COVID-19 survivors than in healthy controls . In another study, a similar finding was seen in patients treated in the hospital for severe COVID-19 who experienced disabling fatigue, with a mean rating of 4.8 out of 10. A high rate of illness‐related fatigue was reported, with 72% of participants in the ICU group and 60.3% in the ward group ,21. ElevWe found no difference in MHI scores in male and female patients. Also, there is no correlation between hospital stay and the MHI total score. A past study found an inverse correlation between the duration of hospital stay, anxiety, depression, and post-traumatic stress disorder (PTSD) .The limitation of our study is that it is a cross-sectional study, and these scores provide objectivity for psychiatric symptoms, but they do not establish a diagnosis of psychiatric illness.The COVID-19 pandemic was a stressful period. Psychiatric symptoms and fatigue were significantly more common in COVID-19 patients after recovery from acute illness. In our study, the mean total Mental Health Inventory (MHI) score, mean subcomponent MHI score, and Modified Fatigue Impact Scale score were significantly higher in COVID-19 patients as compared to healthy controls. The causes for this affection were organic, such as the virus itself involving the nervous system and inflammatory response to the virus. Also, social factors such as isolation, fear of illness, and loss of jobs contributed to psychiatric symptoms and fatigue. The treatment of COVID-19 patients requires managing organic manifestations in the acute stage and attention to psychiatric symptoms and fatigue after recovery from the acute stage of illness.


Proteases comprise the class of enzymes that catalyzesthe hydrolysisof peptide bonds, thereby playing a pivotal role in many aspects oflife. The amino acids surrounding the scissile bond determine thesusceptibility toward protease-mediated hydrolysis. A detailed understandingof the cleavage specificity of a protease can lead to the identificationof its endogenous substrates, while it is also essential for the designof inhibitors. Although many methods for protease activity and specificityprofiling exist, none of these combine the advantages of combinatorialsynthetic libraries, i.e., high diversity, equimolar concentration,custom design regarding peptide length, and randomization, with thesensitivity and detection power of mass spectrometry. Here, we developedsuch a method and applied it to study a group of bacterial metalloproteasesthat have the unique specificity to cleave between two prolines, i.e.,Pro-Pro endopeptidases (PPEPs). We not only confirmed the prime-sidespecificity of PPEP-1 and PPEP-2, but also revealed some new unexpectedpeptide substrates. Moreover, we have characterized a new PPEP (PPEP-3)that has a prime-side specificity that is very different from thatof the other two PPEPs. Importantly, the approach that we presentin this study is generic and can be extended to investigate the specificityof other proteases. However, it is not straightforwardto identify protease substrates without prior knowledge, e.g., withouta clear phenotype in a protease knockout or lack of information fromhomologues in other species. Information about the cleavage specificityof a protease can aid in the identification of endogenous substrates.Moreover, such information is pivotal for inhibitor design or thedevelopment of diagnostic biomarker assays.7Proteases comprise a class of enzymesthat catalyzes the hydrolysisof peptide bonds between amino acids in a polypeptide chain. Throughcleavage of their substrates, proteases play a pivotal role in manyaspects of life, ranging from viral polyprotein processingClostridioides difficile. The first two members, PPEP-1 from the human pathogen difficile,9 and PPtermined 1A. For Ptermined 1A was exnt genes 1B. Based PPEP-3, 1. The mo PPEP-3, 1C.Howev PPEP-3, 1A,B, ham14 Several strategies rely on the identification of protease-generatedprotein neo-N-termini in cells expressing the proteaseof interest compared to controls. For this purpose, positive andnegative selection procedures for the enrichment of N-terminal peptides in combination with quantitative mass spectrometrybased proteomics methods, collectively known as N-terminomics, have been developed.20 However, for an optimal experimental setup for such an experiment,a protease knockout cell line or strain is necessary.A wide variety of methods for protease activityand specificityprofiling has been developed.22 or as a collection of (synthetic) peptides. For the latter, massspectrometry analysis is an attractive readout because it determinesthe signature proteolytic event in a highly specific manner, i.e.,information on the amino acid(s) surrounding the scissile bond isobtained. For example, MALDI-based approaches using synthetic peptidearrays have been used to profile protease activity and specificity,but for such approaches, each peptide requires individual synthesis,treatment, and analysis.24 In addition, proteome-derivedpeptide libraries have been shown to be a rich source of peptidesfor these types of analyses.27 Although with this method a widevariety of potential substrates is tested in a single reaction, theconcentration range of the peptides present may easily span a feworders of magnitude. This may complicate the assessment of whethera product peptide is derived from a very good substrate present ata low concentration or a poor substrate at a high concentration instead.Another method, MSP-MS, uses a small set of synthetic peptides inwhich amino acid pairs are cleverly positioned in order to containa wide variety of potential cleavage sites.28 However, this design was based on the assumption that, for a protease,only the correct positioning of two amino acids is necessary for aprotease to cleave its substrate. Based on the inspection of the listof 228 peptides,29 we predict that noneof these would be cleaved by one of the PPEPs, making MSP-MS not suitablefor specificity profiling of PPEPs and probably other proteases aswell.Otherstrategies seek to identify the protease substrate specificityby making use of peptide libraries, either by phage display technologies30 and such libraries have been used to profileprotease specificity.32 As a read-out for the cleavageof peptides, both fluorescence detection31 and Edmandegradation34 have so far been used.The combination of equimolar peptide concentrations withhighdiversity would be the ideal scenario for the design of a peptidelibrary. This can be achieved by constructing a synthetic combinatorialpeptide library, for example using the one-bead-one-compound approach,Given the beneficial characteristics of mass spectrometry mentionedabove, we reasoned that it would be highly advantageous if this couldbe applied to analyze the product peptides following the incubationof a combinatorial synthetic peptide library with a protease of interestin a single reaction, but this has hitherto not been done. Obviously,the complexity of combinatorial libraries tends to increase dramaticallywhen multiple positions are randomized, thereby impeding MS analysis.Therefore, two aspects are pivotal to make such an approach suitable.First of all, in the design of the library, any prior knowledge orhypothesis about the protease specificity should be utilized. Second,a strategy to enrich, analyze, and identify the product peptides hasto be implemented.Therefore, the aim of the current study wasto develop a novelmethod to study the activity and specificity of a protease, whichcombines the advantages of a combinatorial synthetic peptide library,i.e., high diversity and equimolar peptide concentrations, with thesensitivity and specificity of MS detection. Testing the method withPPEP-1 and PPEP-2 showed results that were in good agreement withprevious data, while some unexpected peptide substrates were observed.Importantly, the new method clearly established PPEP-3 as a genuinePPEP, but also showed that it has a markedly different prime-sidespecificity compared to that of PPEP-1 and PPEP-2.3 30 mg, Waters) and eluted with 400 μLof 50% acetonitrile (v/v) in 0.1% formic acid. Samples were driedby vacuum concentration and stored at −20 °C until furtheruse. If the binding efficiency of the avidin beads is the same forthe peptide library as for biotinylated BSA, and no peptides are lostduring the prewash steps, we expect approximately 20 nmol of peptideyield after the avidin preclearing step.For details onthe synthesis of the combinatorial peptide library, see 3 30 mg, Waters) and eluted with400 μL of 30% acetonitrile (v/v) in 0.1% formic acid. Sampleswere dried by vacuum concentration and stored at −20 °Cuntil further use.Precleaned (sub)libraries(approximately 10 nmol) were incubated with a PPEP (200 ng) for 3h at 37 °C in PBS. A nontreated control was included. After incubation,the samples were loaded onto an in-house constructed column consistingof a 200 μL pipet tip containing a filter and a packed columnof 100 μL of Pierce High Capacity Streptavidin Agarose beads, in order to remove the biotinylated peptides. The flow-throughand four additional washes with 125 μL were collected. The resultingproduct peptides were desalted using reversed-phase solid phase extractioncartridges , and an Exploris480mass spectrometer (Thermo). Fractions were injected onto a cartridgeprecolumn . The gradient was run from 2% to 36% solvent B(20/80/0.1 water/acetonitrile/formic acid (FA) v/v) in 52 min. Thenano-HPLC column was drawn to a tip of ∼10 μm and actedas the electrospray needle of the MS source. The mass spectrometerwas operated in data-dependent MS/MS mode for a cycle time of 3 s,with a HCD collision energy at 30 V and recording of the MS2 spectrumin the orbitrap, with a quadrupole isolation width of 1.2 Da. In themaster scan (MS1) the resolution was 120000, the scan range 350–1600,at standard AGC target at maximum fill time of 50 ms. A lock masscorrection on the background ion www.matrixscience.com) for peptide identification, using the Fixed Value PSM Validator.Mascot searches were with 5 ppm and 0.02 Da deviation for precursorand fragment mass, respectively, and no enzyme specificity was selected.Biotin on the protein N-terminus was set as a variable modification.Raw data analysis was performed in the Xcalibur Qual Browser (Thermo).The EICs displaying all PXPGGLEEF/PPXGGLEEF peptides werecreated by plotting the intensities of the signal corresponding tothe monoisotopic m/z values of both1+ and 2+ charged peptides. To assign individual peptides to theirrespective peaks, each individual peptide was plotted in an EIC andpeptides were assigned to peaks based on retention time and abundance.We generated a database containingall 6859 peptides from the P3 = Val sublibrary, i.e., Ahx-EVXPPXXGGLEEF.The Ahx in all peptide sequences was replaced by a Ile . Raw data were converted to peak lists using ProteomeDiscoverer version 2.4.0.305 (Thermo Electron) and submitted to thein-house created P3 = Val sublibrary database using Mascot v. 2.2.7, while the core proline (P) residues(corresponding to the P1–P1′ positions) are fixed . Second,a biotin was attached to the N-terminus of each peptide, connectedto the rest of the peptide by a small linker  was modified in two ways.First, a six amino acid tail consisting of Gly-Gly-Leu-Glu-Glu-Phe(GGLEEF) was added at the C-terminus 2. This s30 in order to achieve equimolaramounts of each unique peptide. Initially, we synthesized 19 sublibrariesfor which the amino acid at the X corresponding to the P3 position(the first X in the sequence XXPPXX) was known. Each of these sublibrariescontains 6859 peptides (19 × 19 × 19). Since the processof linking biotin to the N-terminus is not 100% efficient, nonbiotinylatedpeptides were also present. To remove these unwanted peptides priorto incubation with a PPEP, the library was precleaned on an avidincolumn (Synthesisof the library was performed using the one-bead one-compound(OBOC) methodncolumn 2.The bincolumn 2.36 Hence, totest the feasibility of our approach, two sublibraries with eithera Val or Lys at this position were incubated with PPEP-1. The formationof products due to proteolysis of substrate peptides present in thelibrary was assessed by using MALDI-FT-ICR MS . As expected, product peptideswere clearly visible when using the P3 = Val library . Although three prolines at P1′-P3′are not found in the endogenous substrates . Even though the signalfor this peptide partially overlapped with the second isotope peakof the PPPGGLEEF peptide , a separate peak for the signalat m/z 944.474 ([M + H]+) was clearly visible. Lastly, a peptide was observed correspondingto either PHPGGLEEF or PPHGGLEEF even though it was hitherto unknownthat PPEP-1 allows for a histidine at the P2′ or P3′position.Although no fragmentation was performed, we couldassign severalproduct peptides when using the P3 = Val library based on the accuratemass and our current understanding of the specificity of PPEP-1 1,11,36 ibstrates 1, it hadOverall, the above results with the two combinatorialsublibrariesdemonstrated the applicability of our approach to detect PPEP activityand study its preference for amino acids surrounding the scissilePro-Pro bond.Following the successful tests of the method with the two sublibrariesand PPEP-1, we applied our method with the full combinatorial peptidelibrary to determine the prime-side substrate specificity of PPEP-1, PPEP-2,and PPEP-3. In order to increase the sensitivity and include fragmentationof the product peptides, samples were analyzed with LC-MS/MS. A nontreatedsample was included as a control.Table S1). Moreover, an enrichment for prolinesat the P2′ and/or P3′ positions was observed (Table S1), in line with what was expected basedon the specificity of PPEP-1 and PPEP-2 (Table S1).Initially, we analyzed theresults by standard database searching against an in-house-generateddatabase (see d PPEP-2 1. For thTable S1). Furthermore, in all caseswhere an isoleucine or leucine was present at the P2′ or P3′position, obviously no distinction could be made by the search algorithm.The results from the database search showed ambiguity in the positionof the proline at the P2′/P3′ position as assigned bythe search algorithm . Also, severalMS/MS spectra were matched with sequences that did not match theexpected 9-mer PXXGGLEEF sequence. For example, some MS/MS spectrawere assigned to the 8-mer sequence KYGGLEEF. However, we arguethat these represent wrong annotations due to the fact that the massand elution time of this peptide is exactly the same as the PPPGGLEEFpeptide, (one of) the highest product peptides observed for all threePPEPs . Second, fragmentationspectra of PXPGGLEEF and PPXGGLEEF peptides showed cleardifferences . Importantly, thespectra of PXPGGLEEF peptides are dominated by the unique PGGLEEF(y7) fragment ion . This was, for example,essential in distinguishing PIPGGLEEF from PPIGGLEEF.The other unique fragment ion of PXPGGLEEF peptides, i.e., theb2 corresponding to PX, appeared less informative becauseit could also represent nondiscriminatory internal fragments. We believethat this was one of the reasons why the results from the databasesearches were often ambiguous. Possibly other search algorithms, ortraining thereof, and new developments for prediction of tandem MSspectra39 could aid in the correct assignmentof product peptides in terms of the amino acids at the second andthird position in the protease-generated product peptides.To substantiate our results, we combined a manual inspection ofthe MS/MS spectra with additional LC-MS/MS analyses of a set of syntheticpeptides. First of all, KYGGLEEF/YKGGLEEF peptides elutemuch earlier than the PPPGGLEEF peptide, and the fragmentationof such peptides is very distinct from PXXGGLEEF peptides, PPPGGLEEFin particular ,in line with what is known about the relative contribution of thesetwo residues to the retention on a reversed phase column.40 Another way to discriminate between these twooptions is by using a stable isotope labeled leucine/isoleucine duringthe synthesis of the library. PXPGGLEEF and PPXGGLEEFpeptide pairs with an identical X residue that we have tested werewell separated, with the exception of PIP and PPI  but given the large number ofisomeric peptides and the extra efforts needed to correctly assignthe amino acid sequence for the PXXGGLEEF peptides as describedabove, we decided to not include these in the further analysis ofthe prime-side specificity. Notwithstanding, they could potentiallyalso provide some information about the P1′ specificity whenlooking at the 11-mer peptides.Basedon these additional analyses, we could refine the resultsfrom the database search and accurately assign the identity and abundanceof the individual product peptides. Because, as opposed to proteome-derivedpeptide libraries,me sites 4. Howeveobtained 4. Becausobtained 2, no var11 was also demonstrated using the full combinatoriallibrary  out of the binding pocket by inducing a kink at the P2′position. Therefore, it was initially hypothesized that the PHPGGLEEF/PPHGGLEEFproduct observed using MALDI-FT-ICR MS (Dabcyl-EVNPPHPD-GluEdans andLysDabcyl-EVNPPPHD-GluEdans) and testedthem with PPEP-1. As expected, based on our library results, PPEP-1is able to hydrolyze a VNP↓PPH, but not a VNP↓PHP peptide. Notwithstanding these exceptions,an overall preference of PPEP-1 for a Pro at the P3′ was observed wasbased on the similarity in genomic organization of PPEP-1 and -2 andtheir endogenous substrates.h PPEP-1 4B. For eh PPEP-1 1, PPEP-2h PPEP-1 4B. Moreoh PPEP-1 4B, againh PPEP-1 1.As mentioned above, we demonstratedfor the first time that PPEP-3is a genuine PPEP that cleaves Pro-Pro bonds 4C. For PCollectively, theabove results showed that all three PPEPs preferredat least one proline at the P2′ or P3′ position. Toemphasize the differences in such product peptides, extracted ionchromatograms (EICs) of every possible PXPGGLEEF/PPXGGLEEFpeptide were constructed 5. Not onFigure S6). Moreover, the overall profiles of the PXPGGLEEF/PPXGGLEEFpeptides look very similar to those presented in To test the reproducibility of our method, we performedthree additionalreplicate experiments with all three PPEPs. The results from theseexperiments show excellent reproducibility S6. Moreo30 not all individual peptides (2.4 million optionswhen using 19 amino acids) will be present in such a library whenstarting with the same number of beads as used for our current synthesis(approximately 1.000.000). Although our experiments with PPEP-1 andthe two P3-sublibraries showed that partial information about thenonprime-side specificity can also be obtained with our method, webelieve that a complementary XXPPXX library, in which the biotin isattached to the C-terminus of the peptides, is essential for a morecomprehensive characterization of the nonprime-side specificity. Sincethe negative selection for substrates proceeds identically to thatof the current library, both libraries can be mixed, allowing forthe profiling of both the prime-side as well as the nonprime-sidein a single experiment.Although in the currentdesign our library is primarily suitableto investigate PPEPs, other proteases that can cleave between thetwo “XX” sequences in the library peptides could alsobe tested, assuming that their activity is not compromised by thepresence of the surrounding prolines. However, we anticipate thatfor other proteases, a different library design would be beneficial,while still using the same central concept of our approach. For example,the addition of the GGLEEF tail as used in our library can be easilytranslated to other libraries as well. Although for the current experimentswith the PPEPs we used a library with two fixed positions, we believethat a strategy using randomization at five sites with only one fixedposition would still be possible and provide a broad understandingof the subsite specificity. However, due to the OBOC principle,11 PPEP-1 was expectedto only tolerate V, I, A, and P at the P2′ position. To substantiateour results with the combinatorial peptide library, we synthesized20 PPEP-1 FRET-quenched substrate peptides that only differed at theP2′ position (LysDabcyl-EVNP↓PXPD-GluEdans) and tested these with PPEP-1 in a time course kineticassay. The results of these experiments are depicted in Based on the endogenous substrates 1 and a sDabcyl-EVNPPVPD-GluEdans andLysDabcyl-EPLPPVPD-GluEdans, representing substratesof PPEP-1 and PPEP-2, respectively . Interestingly, a PPEP-1 W103Y mutant showed very low activitytoward a substrate peptide as compared to WT.46 For PPEP-2, the importance of this residue has been less explored.Nevertheless, our data with PPEP-3 show that a tyrosine at this positionis compatible with PPEP activity. Whether the tyrosine in PPEP-3 thatcorresponds to the Trp-103 in PPEP-1  is responsible for the difference in prime-sidespecificity between PPEP-3 and the other two PPEP-s requires structuralinformation, especially of a substrate-bound cocrystal.The high specificity of each of the PPEPs for aminoacids surroundingthe Pro-Pro motif remains obscure. Remarkably, based on the aminoacid residue at position 103 (Trp-103) in PPEP-1, two groups weredistinguished.G. thermodenitrificans strain NG80-2.Indeed, G. thermodenitrificans encodes for four proteinscontaining four consecutive prolines, two of which contain a signalpeptide for secretion as determined by DeepTMHMM and SignalP 6.0 .48 This lastfeature is thought to be of importance since PPEP-3 itself is predictedto be a secreted protein. One of the identified proteins, GTNG_0956,contains both a putative CAP-domain and an SCP-domain. Admittedly,signal peptide prediction by SignalP 6.0 is inconclusive for thisprotein, since the signal peptide would be short in length and nocleavage site is predicted .In contrast, DeepTMHMM predicts a signal peptide with a higher confidence. The other protein with anXXPPPP motif and a signal peptide is GTNG_3270. This protein is predictedwith high confidence to possess a Sec/SPII signal sequence for integrationin the lipid membrane. However, no functional domains were found forthis protein. The putative PPEP-3 cleavage sites in GTNG_0956 andGTNG_3270 are PSP↓PPP and DNP↓PPP, respectively. Wetested synthetic FRET-quenched peptides containing these motifs forcleavage by PPEP-3 . Collectively, the above datashow that the results from the library experiment resulted in testablehypotheses about possible endogenous PPEP-3 substrates in G. thermodenitrificans strain NG80-2. For PPEP-1 and PPEP-2,the endogenous substrates were identified based on synthetic peptides,bioinformatic predictions, and MS-based secretome analyses.36 Interestingly, none of the sites in the endogenous substrates ofthese two PPEPs has four consecutive prolines, even though for bothproteases the PPPGGLEEF product peptide was (one of) the majorproduct peptides. In order to identify the endogenous substrate ofPPEP-3, additional experiments such as secretome analyses in combinationwith gene knockout studies are needed, although we cannot excludethe possibility that the substrate originates from a different organismthan G. thermodenitrificans.Next, we looked for possible endogenous substrates of PPEP-3. ForPPEP-1 and PPEP-2, genes encoding their substrates are found adjacentto the protease gene 1. Next tnalP 6.0 S8.48 ThiGeobacillus thermodenitrificans) that has a prime-side specificity that is very different from thatof the other two PPEPs.In conclusion, we show for the first timea strategy to study theactivity and specificity of a protease by combining a combinatorialsynthetic peptide library with mass spectrometry. Our method takeseach amino acid into account (with the exception of cysteine) anddirectly showed combinations of amino acids that were tolerated atthe P2′ and P3′ positions. We believe that the strategypresented here is a generic one that can, with a tailored design ofthe library, also be used to explore substrate specificities of otherproteases. Importantly, with the new method we have not only confirmedthe prime-side specificity of PPEP-1 and PPEP-2, but also revealedsome new unexpected peptide substrates. Moreover, we have characterizeda new PPEP (PPEP-3 from


Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models, such as variational autoencoders, which use a variational approximation of the likelihood for inference.We here present the Deep Generative Decoder (DGD), a simple generative model that computes model parameters and representations directly via maximum a posteriori estimation. The DGD handles complex parameterized latent distributions naturally unlike variational autoencoders, which typically use a fixed Gaussian distribution, because of the complexity of adding other types. We first show its general functionality on a commonly used benchmark set, Fashion-MNIST. Secondly, we apply the model to multiple single-cell datasets. Here, the DGD learns low-dimensional, meaningful, and well-structured latent representations with sub-clustering beyond the provided labels. The advantages of this approach are its simplicity and its capability to provide representations of much smaller dimensionality than a comparable variational autoencoder.https://github.com/Center-for-Health-Data-Science/scDGD. The remaining code is made available here: https://github.com/Center-for-Health-Data-Science/dgd.scDGD is available as a python package at  High-throughput methods in biology and medicine produce vast amounts of high-dimensional noisy data that we seek to extract knowledge from. The first step is often to obtain a low-dimensional representation of the data through clustering, PCA analysis or other similar techniques. A good example is gene expression analysis, where we need to compare counts for each of the 10–20 thousand genes between samples or cells. Here, the first analysis is often to visualize the data in two dimensions with UMAP . AlthougThe VAE is the most common type of generative model applied to biological data. In general, VAEs consist of an encoder that maps data to a lower-dimensional probabilistic representation in latent space and a decoder that maps back to the data. The model is trained using maximum likelihood estimation (MLE), which seeks to find a set of parameters that maximize the likelihood of the data. However, the likelihood calculation is intractable due to an integral over the latent space. The solution in the VAE is to approximate the likelihood and maximize a variational lower bound (the ‘ELBO’). This approximation is not always accurate  estimation of all parameters. MAP estimation is the Bayesian analogue of MLE and seeks to find the set of parameters that maximize the posterior, which is the probability of the representations and model parameters given the observed data. There is no intractable step in this estimation, so both model parameters and representations can be estimated directly. The model we are introducing consists of a decoder (or generator), such as a feed-forward neural network, and a distribution over representations with learnable parameters, which are optional. Estimating the representations of the training samples as well as the parameters is straight-forward and can be done by gradient descent as in a non-probabilistic formulation . One of In the following sections, we present the theory of the model and experiments on two very different types of data. We first demonstrate the general functionality of the proposed model on the Fashion-MNIST  is of a higher dimension than the z-space, so the model gives a low-dimensional representation of data. A neural network, the decoder, with parameters z as input and outputs x. These could be the means and standard deviations of independent normal distributions. Thus, the neural network defines a conditional distribution in sample space, Consider a model with observed variable N samples we thus want to find representations Again, i indexing the samplesIf we assume independence among training samples, the log of this joint probability can be obtained from z) for all samples and the model parameters  loss and Adam optimization with betas 0.5 and 0.7. For decoder, representation, and GMM, we choose learning rates Hyperparameters, such as the number of hidden dimensions, capacity, dropout, and the softball prior scale were found through optimization with respect to the validation reconstruction loss. The summary is available as a parallel coordinate plot from weights and biases runs in The VAD and VAE decoder implementations tested here are architecturally identical to the DGDs with a standard Gaussian. The VAE encoder was for simplicity chosen to mirror the decoder, with normal convolutional layers instead of the transposed convolutions. The capacity for all models is 32 and latent dimensions tested are 20, 50, and 100. Instead of the negative log-density of the GMM, the loss for the distribution over latent space is given as the Kullback–Leibler divergence as typical for VI .In the unsupervised model, the log-likelihood The architecture of scDGD is much simpler than that of the Fashion-MNIST DGD. The decoder consists of the input layer with units defined by the latent dimensionality, and three hidden layers of each 100 units, connected to the output layer of (in the case of the PBMC dataset) 32 728 units, representing all transcripts in the data. The number of output units is given by the number of genes with non-zero expression counts in the data. The latent dimensionality was empirically found best to be 20 in terms of validation reconstruction loss and clustering accuracy of the cell types. The depth of the network was also empirically determined. The parallel coordinate summary of the hyperparameter optimization can be found in The normalized expression counts are modelled with a Negative Binomial distribution. Normalization is achieved by dividing the true expression count with the sample’s largest count. We can therefore use Sigmoid activation on the output layer. The reconstruction loss is given as the probability density of the re-scaled expression count and a gene-specific, learned dispersion parameter. Since this parameter is positive definite, we learn its logarithmic counterpart. For training, we use Adam optimization with betas 0.5 and 0.7. For decoder, representation, and GMM, we choose learning rates The component means of the GMM are distributed according to the mollified Uniform, for which we set scale and sharpness to one each. This applies to both 9-component and 18-component model. The standard deviation of the components is initialized with a mean of 0.02 for the 9-component model and 0.01 for the 18-component model, which roughly corresponds to the formula we have introduced in the previous section, but is modified to a fifth of that in order to improve the component separation. Dirichlet alphas are set to two and one for models with 9 and 18 components, respectively. This is motivated by a Dirichlet alpha of one allowing for non-uniform component weights, which is desired in this case of imbalanced cell type classes. In the case of nine Gaussian components, a Dirichlet alpha of one resulted in even more components representing T cells, so we decided to distribute the components more uniformly by increasing alpha.All hyperparameters described above, except for the number of GMM components and of course the output dimensionality, present the empirically determined default parameters of scDGD. We applied this default model to 11 more datasets, in which the output dimensionality was set as the number of transcript found in the data, and the number of Gaussian components was determined by the number of unique cell types identified by CellTypist. Models for all datasets except PBMC (20k) were trained for 800 epochs. PBMC (20k) was trained for 1000 epochs. In all scDGD models, the learning rate of the decoder was reduced to K-means clustering (k = 9) and then evaluated by the ARI. The lower bound is computed on our held out test set. Latent representations and ELBO were computed using scVI’s implemented functions. NLLs and RMSEs were computed based on the models returned mean and dispersion parameters for the test set.An scVI model was trained on our PBMC train set. The model is implemented in Python version 3.8 with the scvi-tools .In this work, we made use of in total 13 publicly available datasets, out of which 12 represent single-cell transcriptomics sets. The dataset referred to as PBMC was used to develop the application of the DGD to single-cell expression data. The remaining single-cell datasets were used to evaluate the performance of scDGD with default parameters in comparison to the popular and successful scVI model with default parameters.The dataset used in our proof of concept is the natural image Fashion-MNIST , Single-Cell Gene Expression Dataset by Cell Ranger 6.1.0 (2019)’ dataset retrieved from 10× Genomics, https://www.10xgenomics.com/resources/datasets/frozen-bmm-cs-healthy-control-1-1-standard-1-1-0. Cell type annotations were approximated using CellTpyist with the ‘Immune_All_Low’ reference model and majority voting. This resulted in 15 distinct cell types.The next dataset, ‘Frozen BMMCs , Single-Cell Gene Expression Dataset by Cell Ranger 1.1.0 (2016)’, contains 1985 bone marrow mononuclear cells with 32 738 features and was retrieved from 10× Genomics, A dataset of 3005 cells from mouse cortex and hippocampus with 19 972 features was published in https://www.10xgenomics.com/resources/datasets/5k-human-jejunum-nuclei-isolated-with-chromium-nuclei-isolation-kit-3-1-standard contained 4392 cells with 36 601 features. Cell type annotations were approximated using CellTpyist with the ‘Cells_Intestinal_Tract’ reference model and majority voting. This resulted in 24 distinct cell types.The dataset of human jejunum ‘5k Human Jejunum Nuclei Isolated with Chromium Nuclei Isolation Kit, Single-Cell Gene Expression Dataset by Cell Ranger 7.0.0 (2022)’ retrieved from 10× Genomics, https://www.10xgenomics.com/resources/datasets/5k-adult-mouse-brain-nuclei-isolated-with-chromium-nuclei-isolation-kit-3-1-standard. Cell type annotations were approximated using CellTpyist with the ‘Developing_Mouse_Brain’ reference model and majority voting. This resulted in seven distinct cell types.This dataset ‘5k Adult Mouse Brain Nuclei Isolated with Chromium Nuclei Isolation Kit, Single-Cell Gene Expression Dataset by Cell Ranger 7.0.0 (2022)’ comprised 7377 cells from the adult mouse brain with 32 285 features and retrieved from 10× Genomics, https://www.10xgenomics.com/resources/datasets/whole-blood-rbc-lysis-for-pbmcs-neutrophils-granulocytes-3-3-1-standard. Cell type annotations were approximated using CellTpyist with the ‘Immune_All_Low’ reference model and majority voting. This resulted in 10 distinct cell types.From the blood of healthy females, a total of 8000 PBMCs, Neutrophils and Granulocytes were extracted into the dataset ‘Whole Blood RBC Lysis for PBMCs and Neutrophils, Granulocytes (3’), Single-Cell Gene Expression Dataset by Cell Ranger 6.1.0 (2021)’, containing 36 601 features. It was retrieved from 10× Genomics, https://www.10xgenomics.com/resources/datasets/10-k-heart-cells-from-an-e-18-mouse-v-3-chemistry-3-standard-3-0-0. Cell type annotations were approximated using CellTpyist with the ‘Immune_All_Low’ reference model. This resulted in three distinct cell types.The 7713 cells with 31 053 features in ‘10k Heart Cells from an E18 mouse (v3 chemistry), Single-Cell Gene Expression Dataset by Cell Ranger 3.0.0 (2018)’ were extracted from an E18 mouse. The data were retrieved from 10× Genomics, https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-3-v3-1-chromium-x-with-intronic-reads-3-1-high contains 11 984 PBMCs from healthy female donors between the ages of 25 and 30, with 36 601 transcripts covered. Cell type annotations were approximated using CellTpyist with the ‘Immune_All_Low’ reference model and majority voting. This resulted in 19 distinct cell types.‘10k Human PBMCs , Single-Cell Gene Expression Dataset by Cell Ranger 6.1.2 (2022)’ retrieved from 10× Genomics, https://www.10xgenomics.com/resources/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0. Cell type annotations were approximated using CellTpyist with the ‘Immune_All_Low’ reference model and majority voting. This resulted in again 19 distinct cell types.Another dataset from female donors aged 25–30 provided was used, containing a total of 23 837 cells with 36 601 features. The ‘20k Human PBMCs , Single-Cell Gene Expression Dataset by Cell Ranger 6.1.0 (2021)’ data were retrieved from 10× Genomics, After excluding the ‘basal-bm1’ library due to poor quality  was retrieved from 10× Genomics, We use the Adjusted Rand Index (ARI)  for evalk-means   based onPython 3.8 is used as the programming basis for all methods. Neural Networks are implemented in Pytorch 1.10 . DimensiModels were trained on NVIDIA TITAN Xp (12 GB), except for scVI on the mouse brain (1M) dataset, which was trained on NVIDIA TITAN RTX (24 GB).It is customary to test generative models on image benchmarks, so we begin our experimental analysis by applying our model to the Fashion-MNIST data. It allows us to understand and evaluate model behaviour, latent space and most importantly sampling quality much better than more complex, biological data. The decoder used for the Fashion-MNIST models is based on the DCGAN  , BojanowOur demonstration of the DGD on image data comes in very handy when next evaluating the generative modelling capabilities of the different approaches. The benefit of image data is that it comes naturally to us to evaluate whether generated data is sensible or not from a qualitative standpoint. In addition, there are many quantitative measures available. We make use of the Fréchet Inception Distance (FID)  ranks thThe FID score of the DGD improves drastically with learning a more complex parameterized distribution, which lends itself naturally to our approach. When using 20 Gaussian components, the FID decreases to 27.25 r (28.0)  and PeerIn order to assess the model performance on scRNA data, we chose to apply it to the PBMC data from K-means clustering of the latent space with nine components.The resulting latent space is depicted as a UMAP projection in K-means clustering with nine components. The reconstruction performance of scVI is on par with our model trained with nine components in terms of goodness of fit, but under-performs in reconstruction accuracy . This process is more time-consuming and computationally expensive than passing new data points through an encoder as in the VAE-based model. However, in our experiments this only took 0.93 s to 36.24 min for 59–130 613 test cells, respectively. In the case where inference is done repeatedly, one could also train an encoder afterwards as described in Altogether, we find that the DGD is a simple and efficient approach to learning low-dimensional representations. Unlike some other generative models, it is fully tractable and bears no architectural restrictions. Although the model can be supervised as we showed on Fashion-MNIST, our scRNA model is completely unsupervised and is suitable for data without existing labels, while achieving a meaningful clustering of the representation on its own. We also observed that a more complex distribution relating to more GMM components can be beneficial for discovering more substructures in the data than provided by class labels. This makes the DGD an ideal candidate for modelling a multitude of biological and medical data. For these types of data, interpretability of the latent space is crucial. Several downstream applications, such as perturbations and pseudo-time, require a low-dimensional and continuous representation. We plan to expand the model to a variety of biological tasks and to include semi-supervised learning and other features that are of interest to generative models for biological and medical data. We further aim to apply this approach to even more extensive single-cell expression datasets and multi-omics data that allow a more thorough investigation of the latent space and can provide meaningful tools for data integration and downstream analysis.btad497_Supplementary_DataClick here for additional data file.


Various predictive models have been developed for predicting the incidence of coronary heart disease (CHD), but none of them has had optimal predictive value. Although these models consider diabetes as an important CHD risk factor, they do not consider insulin resistance or triglyceride (TG). The unsatisfactory performance of these prediction models may be attributed to the ignoring of these factors despite their proven effects on CHD. We decided to modify standard CHD predictive models through machine learning to determine whether the triglyceride-glucose index  and TG that demonstrates insulin resistance) functions better than diabetes as a CHD predictor.Two-thousand participants of a community-based Iranian population, aged 20–74 years, were investigated with a mean follow-up of 9.9 years (range: 7.6–12.2). The association between the TyG-index and CHD was investigated using multivariate Cox proportional hazard models. By selecting common components of previously validated CHD risk scores, we developed machine learning models for predicting CHD. The TyG-index was substituted for diabetes in CHD prediction models. All components of machine learning models were explained in terms of how they affect CHD prediction. CHD-predicting TyG-index cut-off points were calculated.The incidence of CHD was 14.5%. Compared to the lowest quartile of the TyG-index, the fourth quartile had a fully adjusted hazard ratio of 2.32 . A TyG-index > 8.42 had the highest negative predictive value for CHD. The TyG-index-based support vector machine (SVM) performed significantly better than diabetes-based SVM for predicting CHD. The TyG-index was not only more important than diabetes in predicting CHD; it was the most important factor after age in machine learning models..We recommend using the TyG-index in clinical practice and predictive models to identify individuals at risk of developing CHD and to aid in its preventionThe online version contains supplementary material available at 10.1186/s12933-023-01939-9. CHD is a major public health challenge and contributes to the global disease burden. Despite improved prevention methods and treatment techniques , 2, it iVarious CHD predictive models such as Framingham , SystemaA better prediction of CHD may be possible by considering insulin resistance, which occurs years or even decades before diabetes . PreviouMachine learning algorithms have been demonstrated to be extremely useful in predicting cardiovascular disease . Their aIn view of the above, the primary objective of the current study was to investigate the association between the TyG-index and CHD in a 10-year prospective cohort study. The ultimate objective was to modify standard CHD predictive models through machine learning to determine whether the TyG-index functions better than diabetes as a CHD predictor.This cohort study was conducted using data from Yazd Healthy Heart Project (YHHP) a population-based epidemiological study evaluating cardiovascular diseases and metabolic disorders .In YHHP, 100 clusters and 20 families from each cluster were defined, and one adult (aged 20–74 years) from each family was randomly selected for participation and evaluation in the first phase conducted in 2005–2006  .After 10 years of follow-up (2015–2016), the participants were re-invited to Yazd Cardiovascular Research Centre (YCRC) to be re-evaluated .From the 2000 participants, 17 were omitted from the study due to loss during the second phase; from the 1983 individuals participating in the baseline examination, 62 were excluded due to diagnosis of CHD at baseline, 78 due to death during the study, and 308 due to missing data. The remaining 1552 participants  were included in the present study  and conducted based on the Declaration of Helsinki on medical research . InformeBlood samples for laboratory tests were collected after overnight fasting. After centrifugation, serum uric acid (SUA), glucose, and TG were assessed using Pars Azmoon kits . Bionic kits  were used to analyse lipid profiles . All analyses were conducted using a biochemical auto-analyser  .Height was measured in both phases using a stadiometer fixed on a wall with no dents or bumps. While the participants were standing barefoot, their heels, hips, shoulders, and head touching the wall, and their head fixed horizontally to the nearest 0.5 cm. Participants were weighed to the nearest 0.1 kg in the first phase using a digital scale  with minimal clothing and in the second phase using another digital scale . The superior border of the iliac crest and widest part of the buttock were considered to measure waist and hip circumferences, respectively, to the nearest 0.1 cm using a non-stretchable tape.An automatic digital blood pressure monitor  was used to measure blood pressure of the participants’ right arms, while they were in the sitting position. Blood pressure measurements were taken by a trained nurse twice, with an interval of 5 min .Data including demographic features, education, physical activity, smoking habits, family history of premature CHD, and dietary habits were collected by completing questionnaires.Trained interviewers completed questionnaires to assess physical activity, educational attainment, dietary habits and smoking status, in the first phase of the study. For educational attainment, participants were categorized as having a primary, high school, or academic education. Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ) . ParticiCHD events were defined as occurrences of fatal or non-fatal CHD, myocardial infarction (MI), percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), and new angina. The diagnosis of new angina was based on positive findings from the Rose angina questionnaire  in additThe time of outcome for fatal or non-fatal CHD, MI, CABG, positive exercise test, positive cardiac enzymes, and PCI was determined based on medical records. All Rose angina questionnaires  and elecwww.R-project.org). Continuous variables were described as mean ± standard deviation (SD) and compared by independent T-test or ANOVA. Categorical variables were described as numbers (percentage) and compared using chi-square tests.Statistical analyses were performed with SPSS version 24.0 , Python 3, and R version 4.2.2  and 95% CI of CHD incidence in model I were 1.51 0.91–2.51), 1.68 (1.03–2.74), and 2.63 (1.67–4.15) in Q2, Q3, and Q4 groups, respectively. After final adjustment (model IV), HR in Q4 was slightly decreased but still significant. Adjusted HR levels per 1-unit increase in TyG-index were 1.87 (1.59–2.21), 1.70 (1.35–2.14) and 2.16 (1.69–2.77) in the total sample, in men, and in women, respectively  for Q4 compared to Q1. Diabetes medications confounded the association between TyG-index and CHD but dyslipidaemia treatment did not. A TyG-index higher than 9.07 in women and 8.92 in men had the highest sensitivity and specificity simultaneously for predicting CHD (Table 4–16.1 fo. Eliminating diabetes decreased AUC by around 2% in the decision tree, whereas in other models, it did not affect AUC. Depending on the model, TyG-index removal decreased AUC from 1 to 22%. The current study showed that the TyG-index was much better than diabetes in predicting CHD; overall, it was the second most important factor after age.Table The results of this prospective cohort study in a community-based Iranian population followed for 9.9 years indicate that higher a TyG-index is associated with a higher risk of CHD. This association was more evident in females. Additionally, TyG-index outperformed diabetes in CHD prediction models.An association between the TyG-index and CHD was previously confirmed in both observational , 42–49 aPrevious studies have suggested TyG-index cutoff points of 9 and 9.323 for preventing CHD . The resFBS and TG are reflections of insulin resistance in the liver and adipocytes, respectively . A combiStudies have shown that the TyG-index plays a significant role in CHD incidence in women , 58, 59.1c [Previous studies have suggested that the TyG-index predicts cardiovascular events more accurately than hemoglobin A1c . In addi1c , 49 Prev1c , 63, 64,1c , but the1c . Our finThe following strengths of the current study should be noted. This study is the first to evaluate the predictive power of TyG-index in CHD using machine-learning techniques. To the best of our knowledge, the optimal cut-off points had not previously been evaluated in the Iranian population. The community-based prospective nature of our study and definite outcome determination minimize the chance of reverse causation and recall bias. Including both old and young populations was another advantage the current study had over others, as most previous studies recruited middle-aged and older adults. Furthermore, the current study attempted to ameliorate the adjustment of confounders by adding family history of premature CHD, medication use, dietary habits, complete lipid profile components and all anthropometric features to our models. The long follow-up time in the present study acts as a double-edged sword; indeed, it can reflect the lifetime risk of CHD, but on the other hand, our inability to evaluate and control voluntarily health check-ups or lifestyle changes during the ten-year study period may have affected our findings. Compared to previous studies, we had an identical method for defining of CHD by investigating ECGs, cardiac enzymes, using the Rose angina questionnaire, exercise tolerance test, and coronary artery angiogram.This study had several limitations. First, it was embedded in an observational setting, and despite a wide range of adjustments, we cannot rule out the possibility of unmeasured confounders. Single baseline TyG-index investigation may incline our results to intra-individual variation. Second, we may have observed gender-specific results due to the lack of data on menopausal status. Third, only Iranian subjects were included, so our findings might not be generalizable to other countries.The TyG-index can be used in clinical practice and predictive models as a highly valuable index for predicting and preventing CHD, but further studies are needed to validate our findings.Additional file 1: Table S1. Baseline clinical characteristics and biological variables of the participants according to inclusion process. Table S2. Comparison of baseline clinical characteristics and biological variables between genders.